Simons Institute Courses

A Cost Function for Similarity-Based Hierarchical Clustering

Explore a novel cost function for similarity-based hierarchical clustering, examining its theoretical properties and practical implications for machine learning algorithms.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Unbiased Estimation of the Spectral Properties of Large Implicit Matrices

Explore techniques for estimating spectral properties of large implicit matrices, focusing on unbiased methods and their applications in machine learning and computational challenges.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference

Explore structured representations and fast inference by combining graphical models with neural networks, enhancing machine learning capabilities.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Generalization and Equilibrium in Generative Adversarial Nets - GANs

Explore the theoretical foundations of GANs, focusing on generalization and equilibrium concepts in representation learning with Princeton's Sanjeev Arora.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Evaluating Neural Network Representations Against Human Cognition

Explore neural network representations and human cognition, comparing categorization models, psychological spaces, and relational similarities to enhance understanding of cognitive processes.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

The Missing Signal

Explore representation learning and its applications in AI research with insights from Facebook's leading expert.

• YouTube
• 1 hour
• On-Demand
• Free Video

Geometry, Optimization and Generalization in Multilayer Networks

Explore the interplay of geometry, optimization, and generalization in multilayer networks, focusing on representation learning and its applications in deep learning architectures.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Tutorial on Deep Learning IV

Explore advanced deep learning concepts with Carnegie Mellon expert Ruslan Salakhutdinov, covering cutting-edge techniques and applications in machine learning.

• YouTube
• 1 hour
• On-Demand
• Free Video

Tutorial on Deep Learning III

Explore advanced deep learning concepts and techniques with Carnegie Mellon expert Ruslan Salakhutdinov in this comprehensive tutorial from the Foundations of Machine Learning Boot Camp.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Tutorial on Deep Learning I

Explore deep learning foundations, from neural networks to advanced techniques, with insights on feature representation, optimization, and practical applications in computer vision and audio processing.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Nonparametric Bayesian Methods - Models, Algorithms, and Applications IV

Explore probabilistic models for graphs, edge exchangeability, and graph paintbox representations in this advanced machine learning lecture on nonparametric Bayesian methods.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Information-Theoretic Foundations of Generative Adversarial Models

Explore information-theoretic methods for trustworthy machine learning, focusing on generative adversarial models and their foundations for enhanced reliability and performance.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Generalization Bounds for Neural Network Based Decoders

Explore generalization bounds for neural network decoders, examining their implications for information-theoretic approaches in trustworthy machine learning.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Optimal Neural Network Compressors and the Manifold Hypothesis

Explore the intersection of neural network compression and the manifold hypothesis, examining optimal compressors and their implications for machine learning efficiency and performance.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Fairness Without Imputation: A Decision Tree Approach for Fair Prediction With Missing Values

Explore a novel decision tree approach for fair prediction with missing data, addressing fairness concerns without relying on imputation techniques.

• YouTube
• 35 minutes
• On-Demand
• Free Video